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Using unconventional data is key to assessing commercial lending risk

When it comes to commercial lending, credit analysis is an essential part of the process. Banks need to assess the ability of a business to sustain a certain level of debt and repay loans, but a lack of data availability, coupled with human bias, can make this difficult to assess.

A steakhouse on Wall Street catering to investment bankers for example, will be nothing like a vegan restaurant in Greenwich Village. Yes, they may both be restaurants but their clientele, their offerings, their pricing model, and how they respond to different economic stresses such as what’s currently happening with COVID-19, will vary significantly. Equally, as “veganism” is still a relatively new concept, there are unlikely to be many vegan restaurants in the market, making comparable historical data on these types of businesses limited. This lack of data is ironic given that we are inundated on all fronts by data - much of which is difficult to digest and make sense of.

Credit analysis therefore faces three related challenges:

  1. how to find all the data required to answer a credit question
  2. how to make sense of this data - given both the volume of data that exists and the gaps in that data - and derive insights from it
  3. how to apply these insights to understand a business and answer specific credit questions about it

Unless these challenges are met head-on, commercial banks can end up spending the vast majority of their time trying to find data and massage it into a useable format, rather than actually doing analysis. They therefore risk missing crucial insights in the deluge of data points and producing analysis that is weak and not grounded in facts, or worse still, is hampered by their own biases and not applicable to a changing world.

In many ways commercial lending hasn’t changed for decades – banks are still using the same methods and data sources from decades ago, despite the fact that the world has moved on. The internet, for example, brought with it social media, online reviews, lower sales and distribution costs, new revenue streams, etc. As a result of climate change, consumers are becoming more conscious of purchasing goods and services from businesses that conduct themselves in an environmentally and socially-conscious way. The ongoing COVID-19 pandemic is forcing restaurants in several countries and states to close or limit trading to home delivery only. How businesses deal with this crisis will vary significantly.

If we go back to the vegan restaurant in Greenwich Village as an example – let’s say it’s looking for £1.5m of debt finance to open another restaurant. While the bank should of course use financial and operations data to help it assess credit risk, there will be other data sources that should be considered. The location will be key – i.e. it wouldn’t make sense to open it in the middle of the meat packing district(!), the demographic of the surrounding population will also matter as the concept will likely appeal to “millennials” more than older generations, seasonal fluctuations due to trends like “Veganuary” could lead to spikes in popularity, whether the restaurant has an Instagram account to promote its menu and reviews could also impact customer footfall, etc. Equally, if it’s too young a business to have been around during the financial crisis of 2008, banks can look at how it fared during the COVID-19 crisis to determine how it would perform in an extreme down-turn. Did it begin offering home deliveries or take away? Did it pivot from being a restaurant to using its existing supply chain and sites to sell ingredients directly to customers, sort of like a mini-supermarket? Has it since purchased pandemic insurance? Etc.

The only way commercial banks can effectively assess credit risk for businesses in this day and age is by using multiple data sources – including what may be unconventional or previously unavailable – rather than just relying on what they’ve used in the past.

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Sean Hunter

Sean Hunter

CIO

OakNorth

Member since

07 Apr

Location

London

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This post is from a series of posts in the group:

Artificial Intelligence and Financial Services

Artificial Intelligence and Financial Services


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